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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.9K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
10.9K
Conservation of Protein Domains02:26

Conservation of Protein Domains

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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.9K
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

3.7K
ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
3.7K
Protein and Protein Structure02:15

Protein and Protein Structure

79.6K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
79.6K

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相关实验视频

Updated: Jul 6, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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基于深度学习和自我一致性的无旋转蛋白质序列设计.

Yufeng Liu1, Lu Zhang1, Weilun Wang2

  • 1MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.

Nature computational science
|January 4, 2024
PubMed
概括
此摘要是机器生成的。

ABACUS-R是一种新的深度学习方法,比传统方法更有效地为特定的骨干设计蛋白质序列. 这种人工智能驱动的蛋白质设计方法在实验验证中实现了更高的成功率和精度.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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相关实验视频

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科学领域:

  • 计算生物学是一种计算生物学.
  • 蛋白质工程是一种蛋白质工程.
  • 科学领域的人工智能

背景情况:

  • 蛋白质序列设计的深度学习方法在计算上表现有希望,但在实验验证方面落后于传统方法.
  • 现有的方法往往需要复杂的重建和优化侧链结构,限制效率.

研究的目的:

  • 介绍ABACUS-R,这是一种新的深度学习方法,用于设计折叠成预定义蛋白质骨干的氨基酸序列.
  • 在实验环境中克服以前计算方法的局限性.

主要方法:

  • ABACUS-R采用一个使用多任务学习的编码解码器网络.
  • 该模型根据它们的3D局部环境预测中央残留物侧链类型,不包括侧链构造.
  • 编码器-解码器的代应用为目标骨干生成自相一致的序列.

主要成果:

  • 实验结果,包括五个X射线结晶学结构,证明了ABACUS-R的卓越性能.
  • 在成功率和设计精度上,ABACUS-R在成功率和设计精度上都超过了基于功能的最新方法.
  • 简化设计过程消除了对显式侧链结构优化的需求.

结论:

  • ABACUS-R代表了计算蛋白质设计的重大进步.
  • 该方法为设计特定脊柱的功能蛋白序列提供了更有效,更准确的方法.
  • 这种深度学习策略有可能加速蛋白质工程和发现.